
'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_							: Union[str, Any] =							logging.get_logger(__name__)

UpperCAmelCase_							: Any =							{
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}

class 			UpperCAmelCase__    (							A			):
    lowerCAmelCase_					     =		'luke'



    def __init__(						self   :							Tuple,__A   :							str=5_0_2_6_7,__A   :							List[Any]=5_0_0_0_0_0,__A   :							int=7_6_8,__A   :							Optional[int]=2_5_6,__A   :							Optional[Any]=1_2,__A   :							Optional[Any]=1_2,__A   :							Dict=3_0_7_2,__A   :							int="gelu",__A   :							List[Any]=0.1,__A   :							Tuple=0.1,__A   :							Any=5_1_2,__A   :							List[Any]=2,__A   :							int=0.02,__A   :							Union[str, Any]=1e-12,__A   :							Any=True,__A   :							Any=None,__A   :							Optional[Any]=1,__A   :							Any=0,__A   :							Union[str, Any]=2,**__A   :							Any,):
          super().__init__(pad_token_id=__A,bos_token_id=__A,eos_token_id=__A,**__A  )

          _lowerCamelCase					: str		    = vocab_size
          _lowerCamelCase					: List[str]		    = entity_vocab_size
          _lowerCamelCase					: List[Any]		    = hidden_size
          _lowerCamelCase					: Dict		    = entity_emb_size
          _lowerCamelCase					: str		    = num_hidden_layers
          _lowerCamelCase					: str		    = num_attention_heads
          _lowerCamelCase					: str		    = hidden_act
          _lowerCamelCase					: str		    = intermediate_size
          _lowerCamelCase					: Union[str, Any]		    = hidden_dropout_prob
          _lowerCamelCase					: int		    = attention_probs_dropout_prob
          _lowerCamelCase					: Optional[Any]		    = max_position_embeddings
          _lowerCamelCase					: Optional[Any]		    = type_vocab_size
          _lowerCamelCase					: Tuple		    = initializer_range
          _lowerCamelCase					: int		    = layer_norm_eps
          _lowerCamelCase					: Tuple		    = use_entity_aware_attention
          _lowerCamelCase					: Dict		    = classifier_dropout



from functools import lru_cache
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							2
     snake_case											=							set()
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.add(UpperCamelCase_							)
     if n > 1:
          factors.add(UpperCamelCase_							)
     return factors
@lru_cache
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     return len(unique_prime_factors(UpperCamelCase_							)							)
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     return len(set(UpperCamelCase_							)							) in (0, 1)
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							2

     while True:
          # Increment each value of a generated range
          snake_case											=							[base + i for i in range(UpperCamelCase_							)]

          # Run elements through out unique_prime_factors function
          # Append our target number to the end.
          snake_case											=							[upf_len(UpperCamelCase_							) for x in group]
          checker.append(UpperCamelCase_							)

          # If all numbers in the list are equal, return the group variable.
          if equality(UpperCamelCase_							):
               return group

          # Increment our base variable by 1
          base += 1
def      UpperCAmelCase__		(UpperCamelCase_ = 4							):



     """simple docstring"""






     snake_case											=							run(UpperCamelCase_							)
     return results[0] if len(UpperCamelCase_							) else None


if __name__ == "__main__":
 print(solution())




